public static double[] ActivePremium(timeSeries portfolio, timeSeries benchmark, int scale = 252)
{
Initialize();
var expr = Engine.CallFunction("ActivePremium", portfolio.Expression, benchmark.Expression, Engine.RNumeric(scale));
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="asset"></param>
/// <param name="riskfree"></param>
/// <returns></returns>
public static double[] RiskPremium(timeSeries asset, double riskfree)
{
Initialize();
var expr = Engine.CallFunction("CAPM.RiskPremium", asset.Expression, Engine.RNumeric(riskfree));
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <returns></returns>
public static double[] AnnualisedStdDev(timeSeries portfolio)
{
Initialize();
var expr = Engine.CallFunction("sd.annualized", portfolio.Expression);
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="benchmark"></param>
/// <param name="riskfree"></param>
/// <returns></returns>
public static double[] Alpha(timeSeries portfolio, timeSeries benchmark, double riskfree)
{
Initialize();
var expr = Engine.CallFunction("CAPM.alpha", portfolio.Expression, benchmark.Expression, Engine.RNumeric(riskfree));
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="benchmark"></param>
/// <param name="riskfree"></param>
/// <returns></returns>
public static double[] CMLSlope(timeSeries benchmark, double riskfree)
{
Initialize();
var expr = Engine.CallFunction("CAPM.CML.slope", benchmark.Expression, Engine.RNumeric(riskfree));
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="minreturn"></param>
/// <returns></returns>
public static double[] SortinoRatio(timeSeries portfolio, timeSeries minreturn)
{
Initialize();
var expr = Engine.CallFunction("SortinoRatio", portfolio.Expression, minreturn.Expression);
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="benchmark"></param>
/// <param name="riskfree"></param>
/// <returns></returns>
public static double[] InformationRatio(timeSeries portfolio, timeSeries benchmark)
{
Initialize();
var expr = Engine.CallFunction("InformationRatio", portfolio.Expression, benchmark.Expression);
return(expr.AsNumeric().ToArray());
}
public static double[] AnnualisedReturn(timeSeries portfolio, bool geometric = false)
{
Initialize();
var expr = Engine.CallFunction("Return.annualized", new Tuple <string, SymbolicExpression>("R", portfolio.Expression),
new Tuple <string, SymbolicExpression>("geometric", Engine.RBool(geometric)));
return(expr.AsNumeric().ToArray());
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="p"></param>
/// <returns></returns>
public static double[] HistoricalVaR(timeSeries portfolio, double p)
{
Initialize();
var expr = Engine.CallFunction("VaR", new Tuple <string, SymbolicExpression>("R", portfolio.Expression),
new Tuple <string, SymbolicExpression>("p", Engine.RNumeric(p)),
new Tuple <string, SymbolicExpression>("method", Engine.RString("historical")));
return(expr.AsNumeric().ToArray());
}
public void CreateFromTimeSeriesTest()
{
timeSeries ts = null;
/*
* Assert.DoesNotThrow(() =>
* {
* ts = timeSeries.Create(TimeSeriesFactory<double>.SampleData.Gaussian.Create(0.01, 0.02, numseries: 10, freq: DataFrequency.Monthly));
* });
*/
Engine.Print(ts.Expression);
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="p"></param>
/// <returns></returns>
public static double[] ConditionalVaR(timeSeries portfolio, double p)
{
Initialize();
throw new NotImplementedException();
}
public static timeSeries ResidualRisk(timeSeries asset, timeSeries benchmark, double riskfree)
{
Initialize();
throw new NotImplementedException();
}
public static double[] Kurtosis(timeSeries portfolio)
{
Initialize();
throw new NotImplementedException();
}
public static double[] SemiVariance(timeSeries portfolio)
{
Initialize();
throw new NotImplementedException();
}
public static double[] TrackingError(timeSeries portfolio, timeSeries benchmark)
{
Initialize();
throw new NotImplementedException();
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="minreturn"></param>
/// <param name="method"></param>
/// <returns></returns>
public static double[] UpsidePotentialRatio(timeSeries portfolio, timeSeries minreturn, string method)
{
Initialize();
throw new NotImplementedException();
}
private List<double> createInputVector(int counter, timeSeries _ts, forecastSettings _fs, double _maxValue, double _minValue, int _multiplier, double _maxValueT, double _meanValueT, bool replaceOutliers)
{
var lowerBound = counter - (_fs.energyLags.Max() * _multiplier);
if (lowerBound < 0)
{
return null;
}
List<double> InputVector = new List<double>();
InputVector.Add(1.0);
//добавляем все что только можно
//InputVector.Add(Convert.ToDouble(_ts.timestamps[counter].DayOfWeek) / 7);
//InputVector.Add(Convert.ToDouble(_ts.timestamps[counter].Month) / 12);
//cvPair.InputVector.Add(Convert.ToDouble(_ts.timestamps[i].Hour) / 24);
//add status input
for (int j = 0; j < _fs.statusLags.Count; j++)
{
InputVector.Add((Double)_ts.status[counter - _fs.statusLags[j]]);
}
//add energy inputs
double? correctValue = null;
if (replaceOutliers)
{
for (int j = 0; j < _fs.energyLags.Count; j++)
{
//if (_ts.outlierLabel[counter - (_fs.energyLags[j]*_multiplier)] == 0)
{
correctValue = _ts.timeseries[counter - (_fs.energyLags[j] * _multiplier)];
break;
}
}
if (correctValue == null) return null;
}
for (int j = 0; j < _fs.energyLags.Count; j++)
{
double energyValue;
if (replaceOutliers /*&& _ts.outlierLabel[counter - (_fs.energyLags[j]*_multiplier)] != 0*/)
{
energyValue = correctValue.Value;
}
else
{
energyValue = _ts.timeseries[counter - (_fs.energyLags[j] * _multiplier)];
}
InputVector.Add((energyValue - _minValue) / (_maxValue - _minValue));
}
//int k = 0;
//for (int j = 0; j < _fs.energyLags.Count; j++)
//{
// if (_ts.status[counter] == _ts.status[counter - (_fs.energyLags[j] * _multiplier)])
// {
// InputVector.Add(_ts.timeseries[counter - (_fs.energyLags[j] * _multiplier)] / _maxValue - _minValue);
// k++;
// }
//}
//if (k == 0)
//{
// Console.WriteLine("SHIT!!!");
// InputVector = null;
//}
//else
//{
// if (k < _fs.energyLags.Count)
// {
// int dif = Math.Abs(k - _fs.energyLags.Count);
// for (int m = 0; m < dif; m++)
// {
// InputVector.Add(InputVector[_fs.statusLags.Count + 1]);
// }
// }
//}
//add temperature inputs
for (int j = 0; j < _fs.temparatureLags.Count; j++)
{
InputVector.Add(_ts.timeseries[counter - (_fs.energyLags[j] * _multiplier)] / _maxValueT - _meanValueT);
}
//bool ender = false;
//int cx = 0;
//while (!ender)
//{
// if (_ts.status[counter] == _ts.status[counter - (cx + 1) * 7 * _multiplier])
// {
// InputVector.Add(_ts.timeseries[counter - ((cx + 1) * 7 * _multiplier)] / _maxValue - _minValue);
// cx++;
// }
// if (cx == _fs.energyLags.Count)
// {
// ender = true;
// }
//}
return InputVector;
}
private List<double> createOutputVector(int counter, timeSeries _ts, forecastSettings _fs, double _maxValue, double _minValue)
{
List<double> OutputVector = new List<double>();
OutputVector.Add((_ts.timeseries[counter] - _minValue) / (_maxValue - _minValue));
return OutputVector;
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <returns></returns>
public static double CalmarRatio(timeSeries portfolio)
{
Initialize();
throw new NotImplementedException();
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="benchmark"></param>
/// <param name="riskfree"></param>
/// <param name="riskaversion"></param>
/// <returns></returns>
public static double ValueAdded(timeSeries portfolio, timeSeries benchmark, double riskfree, double riskaversion)
{
Initialize();
throw new NotImplementedException();
}
public timeSeries forecast(timeSeries _ts, int _horizon, forecastSettings _fs)
{
timeSeries f = _ts;
_horizon = 0;
//формируем входной вектор
List<double> InputVector = new List<double>();
int i = _ts.timeseries.Count - 1;
InputVector = createInputVector(i, _ts, _fs, maxValue[counter], minValue[counter], n_timestamps, maxValueT[counter], meanValueT[counter], false);
if (InputVector != null)
{
if (mlnset.Count > counter)
{
mlnset[counter].propagate(InputVector);
foreach (var output in mlnset[counter].Outputs)
{
f.timestamps[i] = _ts.timestamps[i];
f.timeseries[i] = (output * (maxValue[counter] - minValue[counter]) + minValue[counter]);
}
counter++;
if (counter == n_timestamps)
{
counter = 0;
}
}
}
//if (_ts.timeseries.Count == trainingSize)
//{
// //extract training set;
// timeSeries trainingTS = _ts.getSample(0, _ts.timeseries.Count);
// int _hour = 0;
// int _minutes = 0;
// for (int i = 0; i < n_timestamps; i++)
// {
// tslocal.Add(trainingTS.getSample(_hour, _minutes, true));
// _minutes = _minutes + 15;
// if (_minutes == 60)
// {
// _hour++;
// _minutes = 0;
// }
// }
// //traing
// Console.WriteLine("train");
// trainingEnsemble(tslocal, _fs);
//}
//else
//{
// if (_ts.timeseries.Count > trainingSize)
// {
// //формируем входной вектор
// List<double> InputVector = new List<double>();
// int i = _ts.timeseries.Count - 1;
// InputVector = createInputVector(i, _ts, _fs, maxValue[counter], minValue[counter], n_timestamps, maxValueT[counter], meanValueT[counter]);
// mlnset[counter].propagate(InputVector);
// foreach (var output in mlnset[counter].Outputs)
// {
// f.timestamps[i] = _ts.timestamps[i];
// f.timeseries[i] = (output + minValue[counter]) * maxValue[counter];
// }
// counter++;
// if (counter == n_timestamps)
// {
// counter = 0;
// }
// }
//}
return f;
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="excess"></param>
/// <returns></returns>
public static double SterlingRatio(timeSeries portfolio, double excess = 0.1)
{
Initialize();
throw new NotImplementedException();
}
/// <summary>
///
/// </summary>
/// <param name="portfolio"></param>
/// <param name="riskfree"></param>
/// <returns></returns>
public static double[] TreynorRatio(timeSeries portfolio, double riskfree)
{
Initialize();
throw new NotImplementedException();
}